The objective of this paper is two-fold. First we propose our novel lightweight model architecture for 1-shot image segmentation. Next we compare the results upon training using 4 gradient-based meta-learning algorithms that have shown good results in image classification. The chosen algorithms are MAML[1]}, Meta-SGD[2]}, FOMAML[1]} and Reptile[4]}. We use the FSS-1000 dataset[5]} for training. We made the choice of using gradient-based meta-learning approaches instead of a metric-based approach as used in the FSS-1000 benchmark because it allowed us to train lightweight models, while retaining comparable accuracy. The chosen algorithms all belong to the MAML[1]} family of algorithms. These algorithms use a meta model to initialize a learner with a set of weights such that, given a task, the learner can converge in a minimal amount of training iterations. Our network architecture is designed to be lightweight. To ensure this, we use a model with minimal downsampling of input feature maps. We use dilated convolutions, instead of downsampling to increase the receptive field of our convolution layers. This approach allows us our model to be much smaller than an encoder-decoder architecture, while still being comparably performant.
